In recent years, the digitalization of images has become increasingly important for storage, manipulation, and distribution purposes. Furthermore, the digitalization of images allows complex manipulations and processing which provides opportunities not previously available. For example, in recent years significant research has been undertaken to provide algorithms for extracting depth information from images. Such depth information may provide three-dimensional (3D) information obtained from a two-dimensional (2D) image. Such 3D information may be used in many applications and may, for example, assist in automatic annotation of images. As another example, the 3D information may be used to generate enhanced images such as full or partial 3D images generated from 2D images.
Specifically, the recovery of depth maps from single images is currently a very active research area in computer vision, with applications that include 3D displays and autonomous robots. Some research is specifically targeted towards the conversion of existing videos and still images into a 3D format that can be rendered on auto-stereoscopic displays.
However, the applications face the basic obstacle that the 2D to 3D conversion task is fundamentally very difficult. Accordingly, most known approaches are heuristic and very few methods attempt the recovery of absolute depth or of the exact ordering of the depths of the various objects forming a scene.
In previous research, various depth cues have been investigated including focus/defocus, texture, scattering, shading, and perspective. However, these approaches tend to work only within restrictive conditions and result in poor quality depth maps that are not suitable for e.g. visualization tasks.
More recently machine learning systems have been attempted which are able to adapt the operation based on training images and known depth data for the training images (e.g. provided by a user). However, these approaches tend to require large training sets of image/depth map pairs, which tend to be available for very specific scenes only.
Hence, an improved approach for generating depth information for an image would be advantageous, and in particular a system allowing increased flexibility, easier implementation, reduced complexity, improved depth information, and/or improved performance would be advantageous.